PECC: Problem Extraction and Coding Challenges

TL;DR

PECC is a new benchmark that tests large language models' ability to interpret narrative problems, extract requirements, and generate code, revealing current limitations especially on math-based challenges.

Contribution

We introduce PECC, a benchmark derived from AoC and Project Euler, to evaluate LLMs' problem understanding and code generation in narrative contexts.

Findings

GPT-3.5-Turbo passes 50% on AoC challenges

Performance drops to 8% on Euler math problems

Models struggle with natural language prompts and complex problems

Abstract

Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions is still unexplored. Addressing this gap, we introduce PECC, a novel benchmark derived from Advent Of Code (AoC) challenges and Project Euler, including 2396 problems. Unlike conventional benchmarks, PECC requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. A key feature of our dataset is the complexity added by natural language prompting in chat-based evaluations, mirroring real-world instruction ambiguities. Results show varying model performance between narrative and neutral problems, with specific challenges in the Euler math-based subset with GPT-3.5-Turbo passing 50% of the AoC challenges and only 8% on the Euler problems. By probing the limits of LLMs' capabilities, our benchmark provides a framework to monitor and assess the subsequent progress of LLMs as a universal problem solver.